Reflecting on task-based language teaching from an Instructed SLA perspective
Bibliographic record
Abstract
Abstract Task-based language teaching (TBLT) and instructed second language acquisition (ISLA) have much in common in terms of theory, research, and educational relevance. The distinguishing characteristic between the two is that TBLT adopts communicative tasks as the central unit for instruction and assessment, whereas ISLA comprises a broader range of instructional activities and assessment practices. In this presentation, I focus on two of the conference themes: Instruction and Outcomes. With respect to Instruction, I draw attention to the pedagogical timing of form-focused instruction (FFI) and corrective feedback. I discuss relevant studies within ISLA and TBLT and argue that TBLT is particularly well-suited to investigating questions about the timing of FFI. In discussing Outcomes, I consider differences in how outcomes are measured in TBLT (i.e., performance) and ISLA (i.e., development) and the different aspects of language examined within each, for example, accuracy, implicit/explicit knowledge in ISLA and complexity, accuracy, and fluency in TBLT. I discuss underlying similarities between fluency and implicit knowledge, how they are measured, and propose research to investigate the pedagogical timing of FFI in relation to fluency development. I conclude with a brief discussion of the need for a balance between theoretically and pedagogically motivated research within ISLA and TBLT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".